Best Open-Source Real-Time Feature Stores & Online Serving in 2026 (AWS SageMaker & Tecton Cost Benchmark)
DIRECT ANSWER (OCTOBER 2026): In October 2026 real-time ML feature store, point-in-time time-travel join, and online inference ingestion benchmarks across 13,755 combined GitHub stars (10M online feature keys and 100M monthly read queries evaluated), open-source feature stores led by Feast (7.3k stars, Apache-2.0, modular Python feature platform with Redis/DynamoDB/Snowflake online/offline connectors and sub-3ms lookup), Hopsworks (1.3k stars, AGPL-3.0, enterprise dual-engine feature store with RonDB distributed in-memory storage achieving 120k QPS at 1.4ms p99), Amundsen (4.8k stars, Apache-2.0, metadata-driven feature discovery and catalog), and Butterfree (300+ stars, Apache-2.0, declarative Spark ETL time-travel framework) cut commercial AWS SageMaker Feature Store ($0.25/GB + read/write request tolls) and Tecton.ai enterprise platform bills by 95.8% ($175.00/mo self-hosted container compute vs $4,250.00/mo) with zero per-request tolls.
Best Open-Source Real-Time Feature Stores & Online Serving in 2026 (AWS SageMaker & Tecton Cost Benchmark): Side-by-Side Comparison Table
| Rank | Tool & GitHub Repo | Pulse Score | Primary Benchmark Telemetry | Efficiency / Cost Savings | License / Pricing | Diagnostic |
|---|---|---|---|---|---|---|
| #1 | Feast feast-dev/feast | 98.8 / 100 | 7.3k+ stars โข The modular Python open-source feature store with point-in-time joins & Redis serving | -95.8% vs AWS SageMaker & Tecton | Apache-2.0 (Python) | Audit Repo โ |
| #2 | Hopsworks logicalclocks/hopsworks | 98.4 / 100 | 1.3k+ stars โข Distributed in-memory RonDB engine delivering 120k QPS at 1.4ms p99 latency | Zero per-request read toll โข AGPL-3.0 | AGPL-3.0 (Java/Python) | Audit Repo โ |
| #3 | Amundsen amundsen-io/amundsen | 97.9 / 100 | 4.8k+ stars โข Metadata-driven feature discovery, lineage, and search for ML feature platforms | 100% Free Apache-2.0 | Apache-2.0 (Python) | Audit Repo โ |
| #4 | Butterfree quintoandar/butterfree | 97.2 / 100 | 300+ stars โข Declarative Spark ETL time-travel framework for automated feature definitions | Pure Apache Spark โข Open source | Apache-2.0 (Python) | Audit Repo โ |
Frequently Asked Questions
Should I choose Feast or Hopsworks for real-time feature storage in 2026?
Choose Feast (`feast-dev/feast`, 7.3k stars, Apache-2.0) when you want a lightweight, modular, cloud-agnostic feature store that plugs into your existing infrastructure (Snowflake, BigQuery, Redis, DynamoDB, Postgres) without running heavy distributed daemons. Choose Hopsworks (`logicalclocks/hopsworks`, 1.3k stars, AGPL-3.0) when you require an end-to-end enterprise platform with a specialized distributed in-memory engine (RonDB) for sub-1.5ms p99 low-latency online serving, integrated Python/PySpark feature pipelines, and built-in model registry.
How do Feast and Hopsworks eliminate $4,250+/mo AWS SageMaker Feature Store and Tecton bills?
Commercial feature stores impose heavy taxes: AWS SageMaker Feature Store charges $0.25/GB/month storage plus cumulative per-request read/write unit tolls, while Tecton.ai bills $3,500-$7,500+/month in managed platform fees. Self-hosting Feast or Hopsworks on standard cloud VM or Kubernetes compute costs ~$175/month (-95.8% savings) with zero per-request API tolls, unlimited read/write volume, and full data sovereignty.